[New Paper] Neural Network–Augmented Physics Models Using Modal Truncation for Dynamic MDOF Systems under Response-Dependent Forces
Published:
Our paper “Neural Network–Augmented Physics Models Using Modal Truncation for Dynamic MDOF Systems under Response-Dependent Forces” has been published in the Journal of Engineering Mechanics.
The work develops a neural network–augmented physics model for multi-degree-of-freedom systems under response-dependent forces using modal truncation.
Citation: Jaehwan Jeon and Junho Song (2025). “Neural Network–Augmented Physics Models Using Modal Truncation for Dynamic MDOF Systems under Response-Dependent Forces.” Journal of Engineering Mechanics.
